ADHD is has long been believed to include brain-based pathophysiology, but the newest approaches to describing atypical brain organization in ADHD are just emerging and are thus far limited to cross-sectional studies. The present study has as its principal aims three objectives. First, it aims to characterize ADHD's brain organization longitudinally from childhood through adolescence, during a period of time in which ADHD youth enter very high risk for negative life outcomes, poor health outcomes, and comorbid behavioral, mood, and substance use disorders. In particular, the project aims to determine specified circuits, unique brain networks, and brain wide topological properties that relate to clinical course and outcome. Second, the proposal aims to bridge these developmental brain features with an enriched conception of phenotype, examining symptom domains, comorbidity, and measures of executive, reward, and emotional functioning in relation to specified brain metrics and targets. Third, it seeks to move the field forward in regard to clinica prediction by considering heterogeneity in two ways. One way is to examine novel typologies of ADHD based on differential brain organization across development. The other way is to utilize multiple methods to enhance prediction of clinical course. The significance of this effort lies bot in its unprecedented ability to characterize ADHD neurobiology over time with methods of brain characterization that have not been examined in ADHD longitudinally in this way before, and in its effort to move brain imaging into the realm of clinical prediction using a longitudinal design. The project is innovative in regard to implementing network and topology features of brain analysis in a multi-wave design, and tightly linking these to well defined, multi-level clinical course. The approach entails tracking of an already developed novel cohort of 376 children in a cross-lagged longitudinal design, enabling characterization of development from age 7-19 years. Brain organization will be operationalized with both diffusion tensor imaging and resting state functional connectivity. Youth will be characterized annually in relation to clinical symptoms, comorbidity, impairment, cognitive functioning, reward discounting, and emotion regulation and functioning. Analytic approaches will span brain circuit, network, and topological measurements using novel graph theoretical analyses to characterize brain systems. Clinical prediction will be undertaken using machine learning methodologies. Clinical typologies will be evaluated using both novel community detection procedures and more standard mixture model analysis of brain features. Finally, latent class trajectory models will be used to identify distint developmental types of ADHD if they exist. The prior grant period has been productive and this work builds on those findings to strengthen inference regarding the relation of brain organization to ADHD clinical features, course, and outcome. The Aims directly match key priorities of the NIMH strategic plan. If successful, the project hopes to break the impasse facing the field with regard to clinical utility of the growing grasp of atypical brain physiology in ADHD.